


'''simple docstring'''






import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase       							=				get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_sentencepiece_available():
		import sentencepiece as sp


__UpperCAmelCase       							=				5
__UpperCAmelCase       							=				10




@require_sentencepiece
@require_tokenizers
class 						a__							(				snake_case__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  List[str]      		= SpeechaTextTokenizer
							lowercase__     :  Tuple      		= False
							lowercase__     :  int      		= True







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														super().setUp()

														lowerCAmelCase__			  =      sp.SentencePieceProcessor()
														spm_model.Load(__snake_case  )
														lowerCAmelCase__			  =      ['''<s>''', '''<pad>''', '''</s>''', '''<unk>''']

														vocab += [spm_model.IdToPiece(id_  ) for id_ in range(len(__snake_case  )  )]
														lowerCAmelCase__			  =      dict(zip(__snake_case   ,       range(len(__snake_case  )  )  )  )

														lowerCAmelCase__			  =      Path(self.tmpdirname  )
														save_json(__snake_case   ,       save_dir / VOCAB_FILES_NAMES['''vocab_file''']  )
														if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																					copyfile(__snake_case   ,       save_dir / VOCAB_FILES_NAMES['''spm_file''']  )

														lowerCAmelCase__			  =      SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )
														tokenizer.save_pretrained(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      '''<pad>'''
														lowerCAmelCase__			  =      1

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case  )   ,       __snake_case  )
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case  )   ,       __snake_case  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]   ,       '''<s>'''  )
														self.assertEqual(vocab_keys[1]   ,       '''<pad>'''  )
														self.assertEqual(vocab_keys[-1]   ,       '''j'''  )
														self.assertEqual(len(__snake_case  )   ,       10_01  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														self.assertEqual(self.get_tokenizer().vocab_size   ,       10_01  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      SpeechaTextTokenizer.from_pretrained(self.tmpdirname  )

														lowerCAmelCase__			  =      tokenizer.tokenize('''This is a test'''  )
														self.assertListEqual(__snake_case   ,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(__snake_case  )   ,       [2_89, 50, 14, 1_74, 3_86]   ,       )

														lowerCAmelCase__			  =      tokenizer.tokenize('''I was born in 92000, and this is falsé.'''  )
														self.assertListEqual(
														    __snake_case   ,       [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']   ,       )
														lowerCAmelCase__			  =      tokenizer.convert_tokens_to_ids(__snake_case  )
														self.assertListEqual(__snake_case   ,       [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]  )

														lowerCAmelCase__			  =      tokenizer.convert_ids_to_tokens(__snake_case  )
														self.assertListEqual(
														    __snake_case   ,       [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']   ,       )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														# fmt: off
														lowerCAmelCase__			  =      {'''input_ids''': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=__snake_case   ,       model_name='''facebook/s2t-small-mustc-en-de-st'''   ,       revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad'''   ,       )




@require_sentencepiece
class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Tuple      		= "valhalla/s2t_mustc_multilinguial_medium"

							lowercase__     :  Tuple      		= "C\'est trop cool"
							lowercase__     :  Optional[int]      		= "Esto es genial"







							@classmethod
							def     __SCREAMING_SNAKE_CASE				(						cls  )			->				Any:
														lowerCAmelCase__			  =      SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name  )
														return cls







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														self.assertEqual(self.tokenizer.lang_code_to_id['''pt''']   ,       4  )
														self.assertEqual(self.tokenizer.lang_code_to_id['''ru''']   ,       6  )
														self.assertEqual(self.tokenizer.lang_code_to_id['''it''']   ,       9  )
														self.assertEqual(self.tokenizer.lang_code_to_id['''de''']   ,       11  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														self.assertEqual(self.tokenizer.vocab_size   ,       1_00_00  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														self.assertIn(__snake_case   ,       self.tokenizer.all_special_ids  )
														lowerCAmelCase__			  =      [ES_CODE, 4, 16_01, 47, 76_47, 2]
														lowerCAmelCase__			  =      self.tokenizer.decode(__snake_case   ,       skip_special_tokens=__snake_case  )
														lowerCAmelCase__			  =      self.tokenizer.decode(generated_ids[1:]   ,       skip_special_tokens=__snake_case  )
														self.assertEqual(__snake_case   ,       __snake_case  )
														self.assertNotIn(self.tokenizer.eos_token   ,       __snake_case  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      '''fr'''
														lowerCAmelCase__			  =      self.tokenizer(self.french_text  ).input_ids
														self.assertEqual(encoded[0]   ,       __snake_case  )
														self.assertEqual(encoded[-1]   ,       self.tokenizer.eos_token_id  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      '''fr'''
														self.assertListEqual(self.tokenizer.prefix_tokens   ,       [FR_CODE]  )

														lowerCAmelCase__			  =      '''es'''
														self.assertListEqual(self.tokenizer.prefix_tokens   ,       [ES_CODE]  )



from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   ):
         snake_case											=							parent
         snake_case											=							1_3
         snake_case											=							7
         snake_case											=							3_0
         snake_case											=							self.seq_length + self.mem_len
         snake_case											=							1_5
         snake_case											=							True
         snake_case											=							True
         snake_case											=							9_9
         snake_case											=							[1_0, 5_0, 8_0]
         snake_case											=							3_2
         snake_case											=							3_2
         snake_case											=							4
         snake_case											=							8
         snake_case											=							1_2_8
         snake_case											=							2
         snake_case											=							2
         snake_case											=							None
         snake_case											=							1
         snake_case											=							0
         snake_case											=							3
         snake_case											=							self.vocab_size - 1
         snake_case											=							0.01
    def 						a_     (			self       ):
         snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )
         snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )

         snake_case											=							None
         if self.use_labels:
              snake_case											=							ids_tensor([self.batch_size, self.seq_length]     ,   self.vocab_size       )

         snake_case											=							TransfoXLConfig(
             vocab_size=self.vocab_size     ,   mem_len=self.mem_len     ,   clamp_len=self.clamp_len     ,   cutoffs=self.cutoffs     ,   d_model=self.hidden_size     ,   d_embed=self.d_embed     ,   n_head=self.num_attention_heads     ,   d_head=self.d_head     ,   d_inner=self.d_inner     ,   div_val=self.div_val     ,   n_layer=self.num_hidden_layers     ,   eos_token_id=self.eos_token_id     ,   pad_token_id=self.vocab_size - 1     ,   init_range=self.init_range     ,   num_labels=self.num_labels     ,   )

         return (config, input_ids_a, input_ids_a, lm_labels)
    def 						a_     (			self       ):
         random.seed(self.seed       )
         tf.random.set_seed(self.seed       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFTransfoXLModel(__snake_case       )

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         snake_case											=							{'''input_ids''': input_ids_a, '''mems''': mems_a}

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         self.parent.assertEqual(hidden_states_a.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(hidden_states_a.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFTransfoXLLMHeadModel(__snake_case       )

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         snake_case											=							{'''input_ids''': input_ids_a, '''labels''': lm_labels}
         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         snake_case					,    snake_case											=							model([input_ids_a, mems_a]       ).to_tuple()

         snake_case											=							{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

         snake_case					,    snake_case											=							model(__snake_case       ).to_tuple()

         self.parent.assertEqual(lm_logits_a.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )

         self.parent.assertEqual(lm_logits_a.shape     ,   (self.batch_size, self.seq_length, self.vocab_size)       )
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a]     ,   [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							TFTransfoXLForSequenceClassification(__snake_case       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_labels)       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         ((snake_case)					,    (snake_case)					,    (snake_case)					,    (snake_case))											=							config_and_inputs
         snake_case											=							{'''input_ids''': input_ids_a}
         return config, inputs_dict






@require_tf
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(
        (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
    )
    __magic_name__         =						() if is_tf_available() else ()
    __magic_name__         =						(
        {
            'feature-extraction': TFTransfoXLModel,
            'text-classification': TFTransfoXLForSequenceClassification,
            'text-generation': TFTransfoXLLMHeadModel,
            'zero-shot': TFTransfoXLForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         if pipeline_test_casse_name == "TextGenerationPipelineTests":
              # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
              # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
              # tokenizer.
              return True

         return False
    def 						a_     (			self       ):
         snake_case											=							TFTransfoXLModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   d_embed=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         self.model_tester.set_seed()
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*__snake_case       )
    def 						a_     (			self       ):
         self.model_tester.set_seed()
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()
         snake_case											=							[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              assert isinstance(model.get_input_embeddings()     ,   tf.keras.layers.Layer       )
              if model_class in list_other_models_with_output_ebd:
                   snake_case											=							model.get_output_embeddings()
                   assert isinstance(__snake_case     ,   tf.keras.layers.Layer       )
                   snake_case											=							model.get_bias()
                   assert name is None
              else:
                   snake_case											=							model.get_output_embeddings()
                   assert x is None
                   snake_case											=							model.get_bias()
                   assert name is None
    def 						a_     (			self       ):
         # TODO JP: Make TransfoXL XLA compliant
         pass
    @slow
    def 						a_     (			self       ):
         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case											=							TFTransfoXLModel.from_pretrained(__snake_case       )
              self.assertIsNotNone(__snake_case       )




    @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''       )
    def 						a_     (			self       ):
         pass






@require_tf
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @unittest.skip('''Skip test until #12651 is resolved.'''       )
    @slow
    def 						a_     (			self       ):
         snake_case											=							TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''       )
         # fmt: off
         snake_case											=							tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]     ,   dtype=tf.intaa       )  # noqa: E231
         # fmt: on
         #  In 1991 , the remains of Russian Tsar Nicholas II and his family
         #  ( except for Alexei and Maria ) are discovered .
         #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
         #  remainder of the story . 1883 Western Siberia ,
         #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
         #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
         #  father initially slaps him for making such an accusation , Rasputin watches as the
         #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
         #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
         #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

         # fmt: off
         snake_case											=							[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # noqa: E231
         # fmt: on
         #  In 1991, the remains of Russian Tsar Nicholas II and his family (
         #  except for Alexei and Maria ) are discovered. The voice of young son,
         #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
         #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
         #  and a group of men to perform magic. Rasputin has a vision and
         #  denounces one of the men as a horse thief. Although his father initially
         #  slaps him for making such an accusation, Rasputin watches as the man
         #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
         #  of the Virgin Mary, prompting him to become a priest.
         #  Rasputin quickly becomes famous, with people, even a bishop, begging for
         #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
         # Nicholas II and his family were discovered. The voice of <unk> young son,
         # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

         snake_case											=							model.generate(__snake_case     ,   max_length=2_0_0     ,   do_sample=__snake_case       )
         self.assertListEqual(output_ids[0].numpy().tolist()     ,   __snake_case       )




